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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Smartphone Application for Badminton sports community 2

Smartphone Application for Badminton sports community

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2nd International Conference on computer, science, Engineering, and Technology, ICComSET 2019

作者： Anwar, Nizirwan Hendroasali, Eric Heryanto Budiyanto, Teddy Hendyan, Willy Warnars, Harco Leslie Hendric Spits Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, there are so many people in Indonesia particularly young people who are interested in playing badminton, but at the end of the day, this game will make boring for young people particularly with the booming of the smartphone game. Literally, it will make the young generation too lazy for outing activity or doing sports and busy with their digital technology, particularly their smartphone. Obviously, we are interested in using the mobile application which can be used to bridge the boring of doing sports mainly badminton sport, wherewith this mobile application is not just only for field badminton or coach booking, but can extend their friendship and community in positive ways. Hopefully, with this mobile application not just only booking for Badminton fields, but beyond that, there will be a mobile application that supports the fans of the badminton sports community when can know each other from different places and make friendship and competition among the members. At the end of the day, we hope this mobile application will support young people to stay away from trouble and drug and to have a positive life with the sport, particularly badminton sport as Indonesian's favorite sport. © Published under licence by IOP Publishing Ltd.

关键词： Sports

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Stock Price Prediction Using LSTM and Search Economics Optimization

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2020年第4期47卷 1-7页

作者： Girsang, Abba Suganda Lioexander, Fernando Tanjung, Daniel Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Long Short-Term Memory (LSTM) has been one of the most popular methods in time-series forecasting. Within the LSTM architecture, there are hyperparameters present that need to be optimized in order to achieve the optimum results. In order to optimize these hyperparameters, a metaheuristic optimization method was used. A metaheuristic algorithm was used as a way to reduce both time and computational complexity. We used the Search Economics algorithm because we found the algorithm quite interesting while being unpopular. The evaluation was carried out by using Root Mean Square Error (RMSE) as the primary metric used for the optimization. The dataset being used in this experiment is the stock price of one of the most well-known financial institutes in Indonesia. The SE-Optimized LSTM was able to create a prediction that did not overfit with RMSE of 538.92, which was great compared to the unoptimized LSTM model with RMSE of 661.041 and ARIMA model with RMSE of 2809.015. © 2020. All Rights Reserved.

关键词： Long short-term memory

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer science and Information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Fuzzy water flow algorithm (FWFA) based ecological decision support model (ecoDSM) for flood control operating system

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AIP Conference Proceedings 2023年第1期2896卷

作者： Ditdit Nugeraha Utama Rachmat Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Intiland Development Jakarta Indonesia 10220

Flood disaster happens periodically in Jakarta. One opportunity that can be maximized to mitigate it is control management of the Manggarai water gate (MWG). The MWG itself is placed in the Ciliwung river in one district of the capital Jakarta. It has a vital role to minimize the impact of flood disasters in Jakarta. The study is a further version of previous research indeveloping a water-flow-like algorithm (WFA) based decision support model (DSM) to mitigate the impact of flood disasters. Theconstructed DSM is practically functioned to control the operation of opening/closing the MWG. The DSM was constructed by combining the previous WFA model with the concept of fuzzy logic (FL). The constructed model is called Fuzzy-WFA (FWFA) ecological DSM (ecoDSM); as it considers several aspects of ecology, e.g. river condition, weather situation, and flood itself. By building the FWFA-ecoDSM, the value of uncertainty can be converted to become crisp logic that is easier to understand. At the end of this research, the FWFA-ecoDSM was implemented in more detail compared with the previous one by producing the decimal numbers with 3 until 6 numbers behind the point; exclusively, in predicting the water level status, where the status used todecide the opening/closing level decision for opening or closing the MWG.

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Data annotation system for intelligent energy conservator in smart building 3

Data annotation system for intelligent energy conservator in...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Chandra, Bloomest Jansen Rahutomo, Reza Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department College of Letters and Science University of Wisconsin MadisonWI53706 United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Historic preservation

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Application of "face Recognition" Technology for Class Room Electronic Attendance Management System 7

Application of "face Recognition" Technology for Class Room ...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Chandra Prabowo, Harjanto Fransditya M., M. Feisal Meyliana Gunawan, Michael Andrew Gaol, Ford Lumban Winanti Universitas Bina Nusantara Mobile Application and Technology Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Bina Nusantara Cyber Security Jakarta Indonesia

ISBN: (纸本)9781665404228

The attendance management system is a system that needed for learning activity in a University. In some University, the attendance management system has been used tapping system which using NFC technology. Actually, that attendance management system is effective for managing each of attendance information. However, until now we can still see many cheats for this attendance system. For example, leave their cards to their classmates and tell them to tap for them. For that, one of the many effective solutions to resolve that problem is to add face recognition technology in the current attendance system. In our experiment, we know that to add face recognition to the current attendance management system surely need a camera and also face dataset for the system. At the beginning, we need at least 9 images with different emotions and face positions to let the system recognizes one's face. To make this system more accurate at recognizing one's face, we would update the face dataset in every face recognizing process. © 2020 IEEE.

关键词： Face recognition

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K-Means Clustering Model to Discriminate Copper-Resistant Bacteria as Bioremediation Agents

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Procedia computer science 2021年 179卷 804-812页

作者： Ika Nurlaila Wahyu Irawati Kartika Purwandari Bens Pardamean Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Department of Biology Education Faculty of Education Pelita Harapan University Tangerang Indonesia 15811 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Copper (Cu) has been excessively used for some valuable commodities and this creates environmental problems. The inorganic element becomes toxic when presents beyond the recommended tolerated concentration. Bacterial-based remediation is seen to be an excellent tool to overcome it as it reduces the copper contamination without yielding any other forms of contamination. There are some pivotal properties in the bacteria render them being considered as bioremediation agents against coppers contamination, namely bioaccumulation and biosorption. In the present study, we question if these bacteria could be clustered into a strong and representative proximity according to their functional properties. Mostly, bacteria are grouped based on their genetic profiles derived from the 16S rRNA sequencing. We propose that our K-Means clustering model can be employed to identify genetically-unlabelled bacteria. But first, a prominent reference should be developed and we are in this phase. We figured out the K-Means clustering model do not pull the same-genus bacteria into the same cluster. Instead, the model gathers into a proximity those isolates with similarity on a functional characteristic termed minimum inhibitory concentration (MIC), regardless their origins and their hierarchy in taxonomy.

关键词： Copper-Resistant Bacteria K-Means Minimum Inhibitory Concentration

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